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Tidyverse Skills for Data Science in R

Roger D. Peng, Carrie Wright, Stephanie Hicks, and Shannon Ellis

Develop insights from data with tidy tools. Import, wrangle, visualize, and model data with the Tidyverse R packages.

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About the Book

This book is intended for data scientists with some familiarity with the R programming language who are seeking to do data science using the Tidyverse family of packages. Through 5 chapters, you will cover importing, wrangling, visualizing, and modeling data using the powerful Tidyverse packages, including the new Tidymodels framework. The Tidyverse packages provide a simple but powerful approach to data science which scales from the most basic analyses to massive data deployments. This book covers the entire life cycle of a data science project and presents specific tidy tools for each stage.

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About the Authors

Roger D. Peng

Roger D. Peng is a Professor of Statistics and Data Sciences at the University of Texas, Austin. Previously, he was Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. His research focuses on the development of statistical methods for addressing environmental health problems and on developing tools for doing better data analysis. He is the author of the popular book R Programming for Data Science and 10 other books on data science and statistics. He is also the co-creator of the Johns Hopkins Data Science Specialization, the Simply Statistics blog where he writes about statistics for the public, the Not So Standard Deviations podcast with Hilary Parker, and The Effort Report podcast with Elizabeth Matsui. Roger is a Fellow of the American Statistical Association and is the recipient of the Mortimer Spiegelman Award from the American Public Health Association, which honors a statistician who has made outstanding contributions to public health. He can be found on Twitter and GitHub at @rdpeng.

Leanpub Podcast

Episode 16

An Interview with Roger D. Peng

Carrie Wright

Carrie Wright is a Senior Staff Scientist at the Fred hutchinson Cancer Center and a member of the Fred Hutchinson Data Science Lab. Previously Carrie was an Assistant Scientist in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and a faculty member of the Johns Hopkins Data Science Lab. Her work focuses on making data science and informatics more approachable and accessible. She is also a faculty member of the open case studies project, the Informatics Technology for Cancer Research (ITCR) Training Network (ITN), and a co-founder of the LIBD rstats club, a community designed to encourage others to learn more about R programming and statistics. Learn more about Carrie at https://carriewright11.github.io/.

Stephanie Hicks

Shannon Ellis

Shannon Ellis is an Associate Teaching Professor in the Cognitive Science Department at UC San Diego.

Contents

Table of Contents

1.Introduction to the Tidyverse

  1. About This Course
  2. Tidy Data
  3. From Non-Tidy –> Tidy
  4. The Data Science Life Cycle
  5. The Tidyverse Ecosystem
  6. Data Science Project Organization
  7. Data Science Workflows
  8. Case Studies

2.Importing Data in the Tidyverse

  1. About This Course
  2. Tibbles
  3. Spreadsheets
  4. CSVs
  5. TSVs
  6. Delimited Files
  7. Exporting Data from R
  8. JSON
  9. XML
  10. Databases
  11. Web Scraping
  12. APIs
  13. Foreign Formats
  14. Images
  15. googledrive
  16. Case Studies

3.Wrangling Data in the Tidyverse

  1. About This Course
  2. Tidy Data Review
  3. Reshaping Data
  4. Data Wrangling
  5. Working With Factors
  6. Working With Dates and Times
  7. Working With Strings
  8. Working With Text
  9. Functional Programming
  10. Exploratory Data Analysis
  11. Case Studies

4.Visualizing Data in the Tidyverse

  1. About This Course
  2. Data Visualization Background
  3. Plot Types
  4. Making Good Plots
  5. Plot Generation Process
  6. ggplot2: Basics
  7. ggplot2: Customization
  8. Tables
  9. ggplot2: Extensions
  10. Case Studies

5.Modeling Data in the Tidyverse

  1. About This Course
  2. The Purpose of Data Science
  3. Types of Data Science Questions
  4. Data Needs
  5. Descriptive and Exploratory Analysis
  6. Inference
  7. Linear Modeling
  8. Multiple Linear Regression
  9. Beyond Linear Regression
  10. More Statistical Tests
  11. Hypothesis Testing
  12. Prediction Modeling
  13. The tidymodels Ecosystem
  14. Case Studies
  15. Summary of tidymodels

About the Authors

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